Description
Implement comprehensive data quality monitoring system to detect anomalies, validate data integrity, and ensure reliable data feeds for trading decisions.
Tasks
Create data validation rules and schemas
Implement real-time anomaly detection algorithms
Build data freshness monitoring (detect stale data)
Create data completeness checks and gap detection
Implement statistical outlier detection
Build automated data correction and flagging system
Create data quality dashboards and alerts
Implement data lineage tracking
Add data source reliability scoring
Create automated data quality reports
Acceptance Criteria
Detects data anomalies within 5 minutes of occurrence
Data validation prevents 99%+ of corrupted data from entering system
Freshness monitoring alerts on data delays >15 minutes
Statistical outlier detection has <5% false positive rate
Data quality dashboard provides real-time system health status
Automated corrections handle 80%+ of common data issues
Quality scores accurately reflect data source reliability
Dependencies
Issue #12 (Data Pipeline Orchestration)
Issue #4 (Twitter API Integration)
Issue #5 (DEX/Raydium Integration)
Description
Implement comprehensive data quality monitoring system to detect anomalies, validate data integrity, and ensure reliable data feeds for trading decisions.
Tasks
Create data validation rules and schemas
Implement real-time anomaly detection algorithms
Build data freshness monitoring (detect stale data)
Create data completeness checks and gap detection
Implement statistical outlier detection
Build automated data correction and flagging system
Create data quality dashboards and alerts
Implement data lineage tracking
Add data source reliability scoring
Create automated data quality reports
Acceptance Criteria
Detects data anomalies within 5 minutes of occurrence
Data validation prevents 99%+ of corrupted data from entering system
Freshness monitoring alerts on data delays >15 minutes
Statistical outlier detection has <5% false positive rate
Data quality dashboard provides real-time system health status
Automated corrections handle 80%+ of common data issues
Quality scores accurately reflect data source reliability
Dependencies
Issue #12 (Data Pipeline Orchestration)
Issue #4 (Twitter API Integration)
Issue #5 (DEX/Raydium Integration)